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To maximize the greenhouse gas (GHG) emission reduction potential of Battery Electric Vehicles (BEVs), it is critical to develop EV dynamic charging management strategies. These strategies leverage the temporal variability in emissions associated with generated electricity to align EV charging with periods of low-carbon power generation. This study introduces a deep neural network tool to enable BEV drivers to make charging sessions align with the availability of cleaner energy resources. This study leverages a Long Short-Term Memory network to forecast individual BEV vehicle miles traveled (VMT) up to two days ahead, using a year-long dataset of driving and charging patterns from 66 California-based BEVs. Based on the predicted VMT, the model then estimates the vehicle's energy needs and the necessity of a charging session. This allows drivers to charge theirvehicles strategically, prioritizing low-carbon electricity periods without risking incomplete journeys. This framework empowers drivers to actively contribute to cleaner electricity consumption with minimal disruption to their daily routines. The tool developed in this project outperforms benchmark models such as recurrent neural networks and autoregressive integrated moving averages, demonstrating its predictive capabilities. To enhance the reliability of predictions, confidence intervals are integrated into the model, ensuring that the model does not disrupt drivers' daily routine trips when skipping non-critical charging events. The potential benefits of the tool are demonstrated by applying it to real-world EV data, finding that if drivers follow the tool’s predictive suggestion, they can reduce overall GHG emissions by 41% without changing their driving patterns. This study also found that even charging in regions with higher carbon-intensity electricity than California can achieve Californian emission levels for EV charging in the short term through strategic management of non-critical charging events. This findingreveals new possibilities for further emissions reduction from EV charging, even before the full transition to a carbon-neutral grid. View the NCST Project Webpage

This paper describes a model for developing highly resolved, time-of-day specific electric vehicle charging demand profiles from travel survey data. Since timing of vehicle charging is dependent on electric vehicle supply equipment (EVSE) availability, four EVSE scenarios are considered: 1) home only, 2) home and workplace only, 3) universal EVSE, and 4) a probabilistic scenario where EVSE availability varies by location. To illustrate the implications of differing demand profiles on power grid operation with high renewable generating capacity, the profiles are in a typical regional economic dispatch model. The results provide a valuable approach for understanding the interactions between vehicle electrification and renewable energy deployment while exploring an updated range of assumptions about EVSE availability and charging behaviors for New York and the six New England states. All scenarios result in increased peak demand and increased generation by non-renewable generating sources. This indicates that incentive mechanisms that influence charging decisions are necessary to attain lower emissions outcomes.View the NCST Project Webpage

The article is devoted to the analysis of the issues of the legal basis of attracting funds from the State Budget of Ukraine, local budgets and EU funds for the implementation of the «green» transition of the energy sector of the economy and the implementation of projects (programs) in the field of energy efficiency. The article found out that the budget legislation of Ukraine enshrines a number of instruments that make it possible to attract budget funds for the implementation of measures and investment projects in the energy sector, namely: A) for the implementation of state investment projects (programs) and other investment projects in the energy sector the following are carried out: 1) budget expenditures directed to the purpose of state investment in state-owned objects using state capital investments; 2) crediting the budget for the implementation of investment projects implemented through state investment; 3) provision of state or local guarantees for the implementation of investment projects in the field of energy provided with budgetary funds of the relevant budget; B) to provide state financial support to participants in the electricity market, who are entrusted with special duties to ensure an increase in the share of electricity production from alternative energy sources (SE «Guaranteed Buyer»), State Budget expenditures are made; C) for the implementation of «green» transition measures implemented at the expense of the State Budget of Ukraine by the Energy Efficiency Fund, according to the relevant budget programs, in the budget program «State Fund for Decarbonization and Energy Efficient Transformation», programs of the Energy Efficiency Fund; D) to realize, at the expense of State and local budgets, longterm obligations for energy service in accordance with energy service contracts concluded by managers of budget funds to improve the energy efficiency of state and communal property; D) to implement at the expense of EU funds in accordance with the Ukraine Facility the measures provided for in the Plan of Ukraine 2024-2027, in the form of expenditures of the State Budget of Ukraine (Component I), and lending to selected investment projects (Component II), the «visibility» of using which Ukraine has to provide It was noted that the budgetary legislation of Ukraine needs to be supplemented in terms of establishing the legal regime of budgetary investment, which has distinct features from spending and lending, establishing the obligation to ensure the «visibility» of the use of EU funds involved in the relevant budgets, carrying out transactions with them, as well as establishing the concept of «EU funds», as it occurs in the budgetary law of the EU member states. [ABSTRACT FROM AUTHOR]

The flexible traction power supply system (FTPSS), integrating power flow controller (PFC), energy storage system, and photovoltaic (PV) system, is able to cancel the neutral section, realize the collaborative operation of multiple traction substations (MTSs), and enhance the efficient utilization of regenerative braking energy (RBE) and renewable energy in electrified railways. However, co‐dispatching energy among MTSs influences the power flow distribution within the traction network, impacting the traction network voltage (TNV). Maintaining optimal energy dispatch while ensuring compliance with TNV standards is critical. Therefore, a two‐stage energy optimal dispatch and control strategy is proposed for FTPSS here. The first stage develops a synergistic energy optimal dispatch model for MTSs considering TNV constraint and power flow constraints, aimed at maximizing the utilization of RBE and PV while minimizing the electricity cost of FTPSS. In the second stage, a control strategy for multiple converters is designed to ensure the dynamic response of FTPSS. Finally, the effectiveness of the proposed strategy and model is substantiated through field load data and experimental validation, and the electricity cost of the FTPSS is reduced by about 21.4%, meanwhile the TNV is maintained in the range of 22.5–29 kV. [ABSTRACT FROM AUTHOR]

The rapid depletion of conventional fuel resources and rising energy demand has accelerated the search for alternative energy sources. Further, the expanding need to use bioenergy crops for sustainable fuel production has enhanced the competition for agricultural land, raising the "food vs. fuel" competition. Considering this, producing bioenergy crops on marginal land has a great perspective for achieving sustainable bioenergy production and mitigating the negative impacts of climate change. C4 crops are dual-purpose crops with better efficiency to fix atmospheric CO 2 and convert solar energy into lignocellulosic biomass. Of these, millets have gained worldwide attention due to their climate resilience and nutraceutical properties. Due to close synteny with contemporary C4 bioenergy crops, millets are being considered a model crop for studying diverse agronomically important traits associated with biomass production. Millets can be cultivated on marginal land with minimum fertilizer inputs and maximum biomass production. In this regard, advanced molecular approaches, including marker-assisted breeding, multi-omics approaches, and gene-editing technologies, can be employed to genetically engineer these crops for enhanced biofuel production efficiency. The current study aims to provide an overview of millets as a sustainable bioenergy source and underlines the significance of millets as a C4 model to elucidate the genes and pathways involved in lignocellulosic biomass production using advanced molecular biology approaches. • Millets, being C4 crops, are considered a model for studying agronomically and economically important traits. • Millets hold great potential as future bioenergy crops due to their climate resilience trait and high water use efficiency. • Millets can be cultivated on marginal land with minimum fertilizer inputs which is essential for resource optimization. • Advanced molecular approaches can help to genetically engineer these crops for optimized biofuel production efficiency. [ABSTRACT FROM AUTHOR]

Photocatalytic conversion of CO 2 into valuable fuels is considered a promising approach for the development of sustainable and renewable energy sources. In this study, we utilized Mo-MOF as a precursor to obtain MoO 3 by pyrolytic derivatization and treated it to obtain MOF-MoO 3-x containing oxygen vacancies as a catalyst for the photocatalytic reduction of CO 2 to CO. MOF derivatization altered the morphology of the MoO 3 , which made the formation of oxygen vacancies easier and facilitated the separation of electron-hole pairs. Meanwhile, the increased specific surface area and the appearance of surface oxygen vacancies enhanced the CO 2 adsorption and activation capabilities of MOF-MoO 3-x. As a result, MOF-MoO 3-x demonstrated a significant enhances in the photoreduction activity of CO 2. Its CO yield was 29.1 μmolg−1h−1, which was 3.7 times higher than that of MOF-MoO 3 and 2.4 times higher than that of H-MoO 3-x. This innovative strategy may provide a new avenue for improving the comprehensive performance of photocatalysts and developing renewable energy sources. MOF-MoO 3-x photocatalytic material derived from Mo-MOF precursor and prepared by ascorbic acid reduction. Compared to the H-MoO 3-x obtained by hydrothermal method, the MOF structure promoted the formation of oxygen defects, the yield of CO reduction by MOF-MoO 3-x had been significantly improved. [Display omitted] [ABSTRACT FROM AUTHOR]

Heat is produced by all machinery, including microprocessors and jet engines, as well as by industrial processes that produce goods from steel to food. There is still a strong desire for alternative energy sources in this day of ecologically conscious and sustainable energy needs. The recovery of heat from waste heat into beneficial electrical energy provides a simple energy source with enormous potential. Thermoelectrics is one of the popular technologies, which can convert heat into electricity, making them useful for power generation. Tin telluride (SnTe), a significant type of newly developed thermoelectric material, has drawn a lot of attention due to its low toxicity and environmentally friendly characteristics. Here, we synthesize Bi/Se co-doped SnTe (Sn 1-x Bi x Te 0.97 Se 0.03 (x = 0, 0.02, 0.04, 0.06)) samples by employing the solid-state metathesis route. The results of this work suggest that the combined action of cationic and anionic substitution of Bi and Se to SnTe matrix could potentially modify the microstructure and band structure of SnTe, thereby effectively improving its electronic, mechanical, and thermal transport properties. In line with the experimental findings, the samples show degenerate semiconducting electronic transport behaviour throughout the temperature range. The maximum Seebeck coefficient is found to be ∼84 μV/K and the total thermal conductivity is reduced to 1.6 W/mK at 573 K in a 6 % Bi-doped sample, which is 2.2 times less than the pristine sample. The Sn 0.94 Bi 0.06 Te 0.97 Se 0.03 sample has a maximum ZT of approximately 0.23 at 573 K, which is three times higher than the pristine sample because of the combined regulation of cation-anion co-doping and an elevated power factor of 936 μW/K2m in Sn 0.96 Bi 0.04 Te 0.97 Se 0.03 sample at 573 K, which is 1.82 times higher than that of pristine SnTe. Considering these findings, it appears that Bi-Se co-doped SnTe is a promising material for thermoelectric applications. [Display omitted] [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose a novel dual-level multisource information fusion approach for handling open multicriteria decision-making (MCDM) issues related to accurately determining the weights of criteria while considering the relative importance of expert opinions, handling heterogeneous information, and selecting an appropriate MCDM ranking method. The proposed approach includes two levels. The first integrates the "analytic hierarchy process (AHP)—full consistency method (FUCOM)", which comprises two phases. In the first phase, criteria weights are assigned using AHP, and FUCOM is employed to correct inconsistencies. In the second phase, the weights are computed for decision-makers. Triangular fuzzy numbers are employed due to their ability to encapsulate all the utilized heterogeneous data types. At the second level of the approach, three well-known distance-based methods, including "technique for order preference by similarity to ideal solution", "visekriterijumska optimizacija i kompromisno resenje", and "multiattributive border approximation area comparison", are fused to process a homogeneous decision matrix and provide comprehensive and robust rankings for alternatives. To develop our novel approach, a renewable energy source for Pakistan's electricity generation is adopted as a case study. The decision matrix contains four alternatives (i.e., hydropower, biomass energy, solar energy, and wind energy) and six main evaluation criteria (i.e., economic, quality of energy, social, political, environmental, and technical), with several subevaluation criteria under each criterion. The findings of the proposed approach were as follows: A2 was in the first rank with a score of 1.7889, and A4 was in the last rank with a score of 0.1199. The rest of the alternatives were distributed between them. The paper's implications include the advancement of decision-making methods, enhancement of decision-making outcomes, and addressing heterogeneous information to highlight the relative importance of experts. [ABSTRACT FROM AUTHOR]

The article discusses the political context of countering climate change in Southeast Asia, highlighting the region's vulnerability to climate change impacts. Despite acknowledging the importance of climate change mitigation and adaptation measures, Southeast Asian states have been slow to respond effectively due to economic priorities and geopolitical complexities. The article argues that the region's focus on economic development and security concerns has hindered significant progress in addressing climate change, leading to a lack of robust investment in renewable energy resources. Additionally, the voluntary nature of climate targets within ASEAN member states further complicates coordinated efforts to combat climate change in the region. [Extracted from the article]

Clean energy and environmental pollution are two key concerns of modern society and are pivotal necessities for the economic, social, and sustainable development of the world. Today around 80% of energy is generated using nonrenewable resources and fossil fuels (oil, gas, coal) which ultimately results in hazardous global emissions. As a clean substitute for fossil fuels, hydrogen has emerged as a promising and renewable energy resource. Utilization of this energy resource requires the development of active, stable, low‐cost environmentally friendly techniques. Water splitting electrolysis is a method for producing clean and efficient hydrogen using an environmentally benign technique that is currently at its most mature stage. Electrolysis is attracting ever‐increasing attention, as it is a promising electrochemical device for hydrogen production from water due to the high conversion efficiency and relatively low energy input required when compared to thermochemical and photocatalytic methods. This paper will outline the need, performance, and insight of anion exchange membrane (AEM) electrolyzer. Recent developments in the design and preparation of AEM. New strategies for activity, stability, and efficiency improvement of AEM. Membrane types, and factors affecting AEM performance in an electrolyzer. This review also discusses the effects, operating characteristics, and energy consumption of electrocatalysts in the AEM electrolyzer. Hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) pathways and mechanisms in acidic and alkaline media. This study seeks to provide a detailed overview of recent accomplishments in the field of the hydrogen economy, particularly electrolysis, to inspire further research and development to address the technology's obstacles. [ABSTRACT FROM AUTHOR]

The achievement of optimal performance is a crucial aspect of renewable energy resources. The study attempts to boost the power conversion efficiency of polycrystalline silicon (Si) photovoltaic cells by the application of anti-reflective coating (ARC). The solgel method is employed to synthesize yttrium oxide (Y 2 O 3). The electro spraying method was utilized to apply the ARC on photovoltaic cells. The effect of coating on PV cells were determined through the structural, electrical, optical and thermal analysis. The antireflective material was uniformly applied over the coated substrate for 45 min (Y-I), 90 min (Y-II), 135 min (Y-III), and 180 min (Y-IV). The sample coated for 135 min showed an optimal power conversion efficiency (PCE) of 21.65 % and 20.09 % in the presence of neodymium light and direct sunlight, respectively, and a minimum electrical resistivity of 3.96 × 10−3 Ω-cm. In addition, Y-III coated cells exhibit a maximum absorbance of 94 %. The Y-III coated photovoltaic cells demonstrated the lowest temperature of 34.6 °C when exposed to an open environment and 40.6 °C when exposed to a controlled environment. The results from numerous studies demonstrated that Y 2 O 3 can be a suitable ARC, which effectively improved the efficiency of photovoltaic cells. [ABSTRACT FROM AUTHOR]

A multi‐input single‐output converter that is based on the impedance network and the standard isolated converters is presented. The topology is named quasi Z‐source full‐bridge isolated converter (qZSFBIC). The proposed topology helps to integrate various renewable power generation systems with a common three‐phase grid‐connected inverter. It was also capable of providing isolation between the input and output circuits in addition to the boost function. The integration of multiple energy sources is achieved using fewer components than conventional converters. As a result, it achieves greater conversion efficiency and has improved circuit characteristics. It offers a larger voltage control range as compared to the conventional ZSC and enhances the input‐output voltage transformation ratio. To determine whether or not the suggested converter is technically feasible, the circuit architecture, operating principle, control mechanism, and simulation results are presented. An improved proportional resonant‐second order general integrator (IPR‐SOGI) has been utilized to provide a gating signal for the voltage source inverter. The 1.5 kW, 400 V, 50 Hz model is designed to authenticate the proposed scheme with qZSFBIC. The results showed that the proposed converter offered double the time of boosting than the conventional ZSC converter and the conversion efficiency is around 89%. [ABSTRACT FROM AUTHOR]

Marine current power is attracting more attention as a renewable energy option. Similar to wind power, marine current power often requires a maximum power point tracking (MPPT) method to optimize power extraction from the free‐flowing water. Research into MPPT methods for marine current power remains limited. Therefore, this paper presents a comprehensive investigation of MPPT methods for marine current power, building upon similar research in wind power. Three methods, namely the optimal tip speed ratio (OTSR), optimal torque (OT), and two variants of the perturb and observe (P&O) method, are explored. Using a simulation model developed for a specific marine current energy converter, where hydrodynamic calculations are coupled with electrical simulations, the study demonstrates that the OTSR method achieves MPPT with a comparably fast convergence time. After a change in water speed, the OTSR method achieves optimal operation within two turbine rotations. Additionally, the P&O methods are shown to achieve MPPT, albeit with a significantly longer convergence time. However, the P&O methods can be more convenient since no model of the system is required, and no water speed measurements are necessary. The proposed implementation of the OT method underperforms but positions the system close to the optimal operational point. [ABSTRACT FROM AUTHOR]

Unbalanced voltages are becoming increasingly common in distribution networks due to the growing integrations of distributed renewable energy resources as well as large loads such as electric vehicle chargers. Such unbalanced voltages introduce significant control challenges as they produce ripples in the controlling signals, impacting the operation of inverters during the unbalanced conditions. Therefore, this paper proposes a controlling algorithm for three‐level inverters aimed at suppressing these ripples on controlling signals; improving the overall performance of inverters, and consequently enhancing the fault ride‐through capability under unbalanced conditions and faults. The proposed controlling algorithm enables inverters to operate in either a balanced current output mode or a suppressed‐ripple active power output mode, depending on the specific event scenario and demands from the grid operators. Current harmonics are suppressed even under severe faults. For improving the voltage balance on the DC capacitors in three‐level inverters, an enhanced method of neutral point current prediction is presented. The proposed method ensures higher balancing accuracy through the use of weighted switching costs, avoiding the requirement of a balancing controller or current extrapolation. Simulation and experimental results demonstrate the effectiveness of the proposed controller for reliable grid operation. [ABSTRACT FROM AUTHOR]

Modern power system networks are under statutory obligations to integrate renewable energy sources (RES). The primary reason is to meet ever‐increasing energy demand and also to curtail environmental pollution by greenhouse gases. However, the higher penetration of RES has the tendency of reducing inertia of overall power system network. Consequently, frequency stability is affected and deviates beyond allowable permissible limits leading to power blackouts, load shedding, and even total system failure. To address the issues associated with reduced inertia, an optimal control of hybrid energy storage system (HESS) has been proposed. HESS is basically a combination of battery and ultracapacitor, where ultracapacitor addresses rapidly varying power component by mimicking inertia while the battery compensates long‐term power variations. Thus, the HESS is effectively controlled to compensate the loss of inertia by regulating its energy flow. For the purpose of improved efficiency and better power management of the HESS, an improvised particle swarm optimization (MPSO)‐based virtual inertia control design has been proposed. The proposed MPSO is utilized to tune the gains of bidirectional dc–dc converter in such a way that improves frequency nadir with faster response to transient disturbances. This proposed method is simulated in MATLAB and its merits are validated in real time using hardware in loop. On analysing of the results, it can be observed that frequency nadir is improved by 48.96% with significant reduction in rate of change of frequency in comparison to conventional particle swarm optimization. [ABSTRACT FROM AUTHOR]

This article introduces innovative protection strategies, including cooperative protection, for power transmission grids amidst a significant shift towards renewable energy sources (RES) such as wind and solar power, as well as inverter‐based resources (IBRs). The method employs a global consensus algorithm to achieve cooperative protection efficiently. This scheme leverages consensus protocols to dynamically oversee distance relay decisions, ensuring efficient fault detection and localization. The decentralized nature of the proposed method enhances robustness and security, while its high‐speed operation is ensured through non‐iterative global consensus algorithms, which provide rapid fault detection and localization crucial for real‐time protection. By incorporating virtual leaders and leveraging existing communication infrastructure, the method achieves superior selectivity in identifying faulty lines, enhancing the reliability and stability of power transmission grids with high‐RES penetration. Notably, the method does not require learning and training processes, making it adaptable to varying power system topologies without the need for extensive retraining or adaptation periods. The proposed methodology enables simultaneous participation in multiple protection zones by establishing interaction rules between agents. Virtual leaders simplify the selection of protection areas, enhancing scalability and fault localization. Simulation results conducted on the IEEE 39‐bus test system validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Increasing the use of renewable energy in microgrids (MGs) offers environmental and economic benefits. However, the unpredictable and intermittent nature of available resources poses challenges for optimal MG scheduling. Hybrid AC–DC microgrids provide a solution, seamlessly integrating renewables while reducing energy losses and improving power grid reliability. Additionally, incentive‐based demand response programs promote flexible energy consumption, further mitigating the variability of renewable generation and enhancing grid stability. This paper investigates the challenges and potential of high renewable penetration in hybrid AC–DC MGs, analysing the role of demand response programs in system optimization. The microgrid's energy management is modelled using MILP, while a Stackelberg game represents the demand response program. These models are integrated to optimize energy management and demand response jointly. Simulations demonstrate the cost‐saving benefits of this integrated framework, achieved through coordinated flexible resource scheduling and incentive‐based demand response programming. [ABSTRACT FROM AUTHOR]

The complexity of power quality (PQ) concerns is intensifying in tandem with the proliferation of inverter‐based renewable energy systems. The integration of power electronic devices within the distribution network exacerbates the complexity and introduces greater temporal variability to signal components. This paper introduces an advanced, online optimization technique for the decomposition and identification of PQ disturbances (PQDs). Initially, an improved variational mode decomposition (IVMD) method is presented, leveraging an energy ratio criterion for precise decomposition of concurrent PQDs. Subsequently, utilizing the characteristic attributes derived from IVMD, an optimized support vector machine (OSVM) algorithm is developed through the synthesis of diverse kernel functions. The OSVM strategically employs distinct kernel functions to augment the discriminability of the feature set. The synergy of IVMD and OSVM enables the detection of multiple PQDs, remarkably even with a minimal amount of training data. A series of experiments have been conducted to validate the effectiveness of the proposed methodology. The results corroborate that the formulated framework exhibits robust learning capabilities and a high degree of resistance to noise interference. Moreover, the hardware platform experiments prove that the proposed method has a satisfactory real‐time performance for its practicability. This paper introduces an advanced online optimization technique for decomposing and identifying power quality disturbances (PQDs). Leveraging an improved variational mode decomposition method and an optimized support vector machine algorithm, the framework enables the detection of multiple PQDs even with minimal training data. [ABSTRACT FROM AUTHOR]

Since the product contains no carbon-based substances and can be driven by non-carbon-based electricity, electrocatalytic water splitting is considered to be among the most effective strategies for alleviating the energy crisis and environmental pollution. This process helps lower greenhouse gas emissions while also supporting the shift toward renewable energy sources. The anodic oxygen evolution reaction (OER) involves a more complex multi-electron transfer process, which is the principal limiting factor in overall water splitting. Extensive research has demonstrated that the controlled design of effective electrocatalysts can address this limitation. In this study, a previously unreported covalent organic framework material (COF-IM) was synthesized via a post-synthetic modification strategy. Notably, COF-IM contains imidazole nitrogen metal active sites. Transition metal-coordinated COF-IM@Co can function as a highly effective electrocatalyst, exhibiting a lower overpotential (403.8 mV@10 mA cm−2) in alkaline electrolytes, thereby highlighting its potential for practical applications in energy conversion technologies. This study offers new perspectives on the design and synthesis of COFs, while also making substantial contributions to the advancement and application of OER electrocatalysts. [ABSTRACT FROM AUTHOR]

Vanadium redox flow batteries (VRFBs) have emerged as a promising energy storage solution for stabilizing power grids integrated with renewable energy sources. In this study, we synthesized and evaluated a series of zeolitic imidazolate framework-67 (ZIF-67) derivatives as electrode materials for VRFBs, aiming to enhance electrochemical performance. Four materials—Co/NC-700, Co/NC-800, Co3O4-350, and Co3O4-450—were prepared through thermal decomposition under different conditions and coated onto graphite felt (GF) electrodes. X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) analyses confirmed the structural integrity and distribution of the active materials. Electrochemical evaluations revealed that electrodes with ZIF-67-derived coatings exhibited significantly lower charge transfer resistance (Rct) and higher energy efficiency (EE) compared to uncoated GF electrodes. Co/NC-800//GF delivered the highest energy efficiency and discharge capacity among the tested configurations, maintaining stable performance over 100 charge–discharge cycles. These results indicate that Co/NC-800 holds great potential for use in VRFBs due to its superior electrochemical activity, stability, and scalability. [ABSTRACT FROM AUTHOR]

This study investigates the valorization of spent coffee grounds (SCGs) through protein extraction and their application in mycelium-based packaging and renewable energy pellets. Three extraction methods—mechanical stirring, ultrasound-assisted, and CO2-assisted extraction—were applied to SCGs. CO2-assisted extraction yielded the highest protein content at 34.24%, followed by mechanical stirring (31.46%) and ultrasound-assisted extraction (28.51%). The total polyphenol content and antioxidant capacity were also highest in the CO2 extracts, suggesting that this method preserves bioactive compounds most effectively. After protein extraction, SCGs were tested as a component in mycelium-based packaging, with results showing an apparent density of 0.551 g/cm3 and compression resistance of 3.354 MPa, indicating its suitability for structural applications. The energy value of SCGs remained high, with a calorific value of 19,887 J/g DW, slightly decreasing after extraction but still sufficient for renewable energy production. These findings highlight the potential of SCGs as a multi-functional resource, contributing to sustainable solutions across various industries. [ABSTRACT FROM AUTHOR]

In this paper, a design method using a neural network of a zero-voltage-switching buck quasi-resonant DC–DC converter is presented. The use of this innovative approach is justified because the design of quasi-resonant DC–DC converters is more complex compared to that of classical DC–DC converters. The converter is a piecewise linear system mathematically described by Kirchhoff's laws and represented through switching functions. In this way, a mathematical model is used to generate data on the behavior of the state variables obtained under various design parameters. The obtained data are appropriately normalized, and a neural network is trained with them, which in practice serves as the inverse model of the device. An example is considered to demonstrate how this network can be used to design the converter. The key advantages of the proposed methodology include reducing the development time, improving energy efficiency, and the ability to automatically adapt to different loads and input conditions. This approach offers new opportunities for the design of advanced DC–DC converters in industries with high efficiency and performance requirements, such as the automotive industry and renewable energy sources. [ABSTRACT FROM AUTHOR]

The advancement of hydrogen technology and rising environmental concerns have shifted research toward renewable energy for green hydrogen production. This study introduces a novel tri-level transaction methodology for microgrid clusters, addressing uncertainties and price fluctuations in hydrogen. We establish a comprehensive microgrid topology with distributed power generation and hydrogen production facilities. A polygonal uncertainty set method quantifies wind and solar energy uncertainties, while an enhanced interval optimization technique refines the model. We integrate a sophisticated demand response model for hydrogen loading, capturing users' behavior in response to price changes, thereby improving renewable energy utilization and supporting economically viable management practices. Additionally, we propose a tri-level game-theoretic framework for analyzing stakeholder interactions in microgrid clusters, incorporating supply–demand dynamics and a master–slave structure for microgrids and users. A distributed algorithm, "KKT & supply-demand ratio", solves large-scale optimization problems by integrating Karush–Kuhn–Tucker conditions with a heuristic approach. Our simulations validate the methodology, demonstrating that accounting for uncertainties and dynamic hydrogen prices enhances renewable energy use and economic efficiency, optimizing social welfare for operators and economic benefits for microgrids and users. [ABSTRACT FROM AUTHOR]

The power-to-X strategy for passenger car applications offers a viable solution for using the surplus electrical power from renewable energy sources instead of exporting it to the grid. The innovative system proposed in this study allocates surplus electrical power from a building-integrated biomass-based Combined Cooling Heating and Power (CCHP) system to on-site applications and evaluates the energetic and economic benefits. The system comprises two key components: a 50 kW electric vehicle (EV) charging station for EVs and a 50 kW alkaline electrolyzer system for on-site hydrogen production, which is later dispensed to fuel cell electric vehicles (FCEVs). The primary goal is to decrease the surplus of electricity exports while simultaneously encouraging sustainable transportation. The system's economic viability is assessed through two scenarios of fuel (e.g., biomass) supply costs (e.g., with and without fuel market costs) and compared to the conventional approach of exporting the excess power. The key findings of this work include a substantial reduction in surplus electricity exports, with only 3.7% allocated for EV charging and 31.5% for hydrogen production. The simple payback period (SPB) is notably reduced, enhancing economic viability. Sensitivity analysis identifies the optimal hydrogen system, featuring a 120 kW electrolyzer and a 37 kg daily hydrogen demand. The results underscore the importance of prioritizing self-consumed energy over exports to the national grid, thereby supporting integrated renewable energy solutions that enhance local energy utilization and promote sustainable transportation initiatives. [ABSTRACT FROM AUTHOR]

The modular multilevel converter (MMC) plays an important role in large-scale renewable energy integration and transmission, and it can also operate in standalone mode, powering AC passive loads. This paper focuses on the impact of load variation on the stability of the MMC. First, the impact of load variation on the MMC transfer function is analyzed in detail using the harmonic state-space (HSS) modeling method. Then, by means of the impedance-based stability analysis method, it is found that the MMC tends to become unstable with the increase in inductive loads. If the controller is not well-designed, the system may fail when loads change. Therefore, the worst-case design is used to guarantee the overall system's stability under all load conditions. The impact of traditional proportional resonant (PR) controller parameters on the system's stability is analyzed, revealing that the stability margin and control performance of the controller are limited. Thus, an improved controller structure with an additional series of compensators is proposed. Extensive simulation results in MATLAB/Simulink R2024a verify the analysis of this work and the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Reducing maritime greenhouse gas (GHG) emissions is challenging. As efforts to address climate change are gaining momentum, reducing the environmental impact becomes crucial for maritime short-to-medium-term sustainability. The International Maritime Organisation (IMO) has adopted Life Cycle Assessment (LCA) guidelines for estimating GHG emissions associated with alternative fuels. This paper proposes an examination of the latest IMO-adopted LCA guidelines, comparing them with existing methodologies used for the transport sector. By scrutinising these guidelines, the paper aims to provide a better understanding of the evolving landscape for GHG emission estimation within the maritime sector. The paper presents a case study that applies the newly established LCA guidelines to a promising alternative fuel pathway, i.e., waste-wood-derived pyrolysis oil. Pyrolysis oil offers an attractive option, leveraging waste materials to generate a sustainable energy source. The environmental impact of pyrolysis oils is quantified according to the IMO LCA guidelines, offering insights into its viability as a cleaner alternative as marine fuel. The results show the large potential for GHG savings offered by this pathway: upgraded pyrolysis oil can deliver significant GHG savings, and this contribution is linearly dependent of its energy share when blended with standard Heavy Fuel Oil. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of Micro Gas Turbines (MGTs) in the global shift towards low-carbon energy systems, particularly focusing on their integration within microgrids and distributed energy generation systems. MGTs, recognized for their fuel flexibility and efficiency, have yet to achieve the commercialization success of rival technologies such as Internal Combustion Engines (ICEs), wind turbines, and solar power (PV) installations. Through a comprehensive review of recent techno-economic assessment (TEA) studies, we highlight the challenges and opportunities for MGTs, emphasizing the critical role of TEA in driving market penetration and technological advancement. Comparative analysis with ICE and RES technologies reveals significant gaps in TEA activities for MGTs, which have hindered their broader adoption. This paper also explores the learning and experience effects associated with TEA, demonstrating how increased research activities have propelled the success of ICE and RES technologies. The analysis reveals a broad range of learning and experience effects, with learning rates (α) varying from 0.1 to 0.25 and experience rates (β) from 0.05 to 0.15, highlighting the significant role these effects play in reducing the levelized cost of energy (LCOE) and improving the net present value (NPV) of MGT systems. Hybrid systems integrating MGTs with renewable energy sources (RESs) and ICE technologies demonstrate the most substantial cost reductions and efficiency improvements, with systems like the hybrid renewable energy CCHP with ICE achieving a learning rate of α = 0.25 and significant LCOE reductions from USD 0.02/kWh to USD 0.017/kWh. These findings emphasize the need for targeted TEA studies and strategic investments to unlock the full potential of MGTs in a decarbonized energy landscape. By leveraging learning and experience effects, stakeholders can predict cost trajectories more accurately and make informed investment decisions, positioning MGTs as a competitive and sustainable energy solution in the global energy transition. [ABSTRACT FROM AUTHOR]

In this research, ethe performance of bifacial photovoltaic (PV) modules under varying background conditions is explored, specifically green grass, brown clay, and white gravel, on a sunny day. By leveraging both exergy and energy analysis, this research aims to provide a comprehensive evaluation of bifacial module efficiency compared to traditional monofacial modules. The experimental setup simulates diverse installation environments, including rooftops and ground-mounted systems, by varying background reflectance. Key performance metrics such as energy yield, exergy yield, and overall efficiency were measured. The findings reveal that bifacial modules installed over white gravel backgrounds achieve the highest exergy profile and efficiency during peak solar radiation periods, attributed to the enhanced reflectivity of white gravel. These insights can inform strategic decisions regarding the selection and placement of bifacial modules to optimize energy and exergy outputs in real-world scenarios. This study contributes valuable knowledge to the advancement of renewable energy technologies, offering guidance for researchers, developers, and policymakers focused on sustainable energy solutions. [ABSTRACT FROM AUTHOR]

In 2020, the transportation sector was the second largest source of carbon emissions in the UK and in Newcastle upon Tyne, responsible for about 33% of total emissions. To support the UK's target of reaching net zero emissions by 2050, electric vehicles (EVs) are pivotal in advancing carbon-neutral road transportation. Optimal EV charging requires a better understanding of the unpredictable output from on-site renewable energy sources (ORES). This paper proposes an integrated EV fleet charging schedule with a proximal policy optimization method based on a framework for deep reinforcement learning. For the design of the reinforcement learning environment, mathematical models of wind and solar power generation are created. In addition, the multivariate Gaussian distributions derived from historical weather and EV fleet charging data are utilized to simulate weather and charging demand uncertainty in order to create large datasets for training the model. The optimization problem is expressed as a Markov decision process (MDP) with operational constraints. For training artificial neural networks (ANNs) through successive transition simulations, a proximal policy optimization (PPO) approach is devised. The optimization approach is deployed and evaluated on a real-world scenario comprised of council EV fleet charging data from Leicester, UK. The results show that due to the design of the rewards function and system limitations, the charging action is biased towards the time of day when renewable energy output is maximum (midday). The charging decision by reinforcement learning improves the utilization of renewable energy by 2–4% compared to the random charging policy and the priority charging policy. This study contributes to the reduction in battery charging and discharging, electricity sold to the grid to create benefits and the reduction in carbon emissions. [ABSTRACT FROM AUTHOR]

The document discusses the application of optimization principles in the planning and operation of active distribution networks (ADNs) to handle distributed generation resources like renewables, battery energy storage systems (BESSs), and electric vehicles (EVs). Various optimization techniques are used to address challenges such as voltage regulation, loss minimization, demand response, and renewable energy integration, aiming to develop efficient, flexible, and sustainable networks transitioning towards smart grids. The document also highlights the importance of multi-objective optimization approaches in balancing conflicting objectives like cost, reliability, and sustainability, making objective functions essential tools for modern energy distribution systems. [Extracted from the article]

With the proposed "double carbon" target for the power system, large-scale distributed energy access poses a major challenge to the way the distribution grid operates. The rural distribution network (DN) will transform into a new local power system primarily driven by distributed renewable energy sources and energy storage, while also being interconnected with the larger power grid. The development of the rural DN will heavily rely on the construction and efficient planning of the microgrid (MG) within the agricultural park. Based on this, this paper proposes a two-stage optimal scheduling model and solution strategy for the microgrid distribution network with multi-source agricultural load aggregation. First, in the first stage, considering the flexible agricultural load and the market time-of-use electricity price, the economic optimization is realized by optimizing the operation of the schedulable resources of the park. The linear model in this stage is solved by the Lingo algorithm with fast solution speed and high accuracy. In the second stage, the power interaction between the MG and the DN in the agricultural park is considered. By optimising the output of the reactive power compensation device, the operating state of the DN is optimised. At this stage, the non-linear and convex optimization problems are solved by the particle swarm optimization algorithm. Finally, the example analysis shows that the proposed method can effectively improve the feasible region of safe operation of the distribution network in rural areas and improve the operating income of a multi-source agricultural load aggregation agricultural park. [ABSTRACT FROM AUTHOR]

The increase in new energy grid connections has reduced the supply-side regulation capability of the power system. Traditional economic dispatch methods are insufficient for addressing the flexibility limitations in the system's balancing capabilities. Consequently, this study presents a day-ahead scheduling method for renewable energy systems that balances flexibility and economy. This approach establishes a dual-layer optimized scheduling model. The upper-layer model focuses on the economic efficiency of unit start-up and shut-down, utilizing a particle swarm algorithm to identify unit combinations that comply with minimum start-up and shut-down time constraints. In contrast, the lower-layer model addresses the dual uncertainties of generation and load. It employs the Generalized Polynomial Chaos approximation to create an opportunity-constrained model aimed at minimizing unit generation and curtailment costs while maximizing flexibility supply capability. Additionally, it calculates the probability of flexibility supply-demand insufficiency due to uncertainties in demand response resource supply and system operating costs, providing feedback to the upper-layer model. Ultimately, through iterative solutions of the upper and lower models, a day-ahead scheduling plan that effectively balances flexibility and economy is derived. The proposed method is validated using a simulation of the IEEE 30-bus system case study, demonstrating its capability to balance system flexibility and economy while effectively reducing the risk of insufficient supply-demand balance. [ABSTRACT FROM AUTHOR]

This study examines the optimal energy mix for industrial complexes by incorporating renewable energy systems, decarbonization strategies, and sector coupling technologies. Using data from the Balan Industrial Complex in Korea, five energy scenarios were evaluated, ranging from conventional systems (Scenario 1) to advanced renewable configurations (Scenario 5). The results show that Scenario 5, which integrates sector coupling systems and decarbonization technologies, is the most cost-effective and environmentally sustainable. Scenario 5 achieves the lowest Net Present Cost (NPC), and significantly reduces CO2 emissions. Furthermore, an analysis of electricity prices and CO2 costs from Korea, the United States, and Germany highlights the critical role of regional electricity tariffs and carbon pricing in determining the economic feasibility of energy systems. While renewable setups require higher initial investments, Scenario 5 proves to be the most economically viable over time, offering both cost savings and environmental benefits. These findings provide valuable insights for policymakers and industry leaders, emphasizing the importance of customized strategies to optimize energy systems in industrial applications. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to present family farms as consumers and producers of renewable energies which provide them with an opportunity to reduce operating costs. The time scope of the study is 2014–2022, and the Farm Accountancy Data Network is used as the data source. The following research methods were employed: comparative and descriptive analysis, intensity indicators, ranking assignment and panel regression. Based on the values of energy output and energy costs, the rankings revealed a strong position of the Netherlands and Germany. As demonstrated by the study, energy production and consumption volumes depend on the farms' economic size, but are not impacted by production type. Another finding is that energy production covers only one-third of its costs. Also, both production volumes and costs were on a growth path on a year-over-year basis, with similar growth ratios. The European Union's leaders in energy consumption and production are the Netherlands, Germany, Belgium, Denmark, Czech Republic, Hungary, Luxembourg, Slovakia and Sweden. The study included the structuring of panel models for energy output and costs and identified their determinants. Energy output depends on total inputs and grows as they grow. Energy costs, in turn, are related to utilized agricultural area, total output and family farm income. An important limitation of this study is that FADN is a provider of high-level data. Hence, it is impossible to tell what specific sources of renewable energy are used by farms, and how they are affected by such exogenous factors as climate, earmarked subsidies or energy policy. The findings from this study are discussed in the context of the European Commission's recommendations laid down in the Bioeconomy Strategy of the EU (2013), the Seventh Environment Action Program, the New Innovation Agenda of the European Union, the Report "Transforming Our World: the United Nations 2030 Agenda for Sustainable Development" and the Circular Economy Action Plan. [ABSTRACT FROM AUTHOR]

This article examines the prospects of Scheffler solar receivers integrated into renewable energy power plants for civil applications. This kind of solar receiver can offer satisfactory energetic performance with acceptable energy conversion efficiency when compared to other technologies to harness solar energy since the high-quality focal receiver can reduce heat losses also supposing great levels of evaporation temperature. In this research, energetic optimization and a broad assessment of Scheffler-type solar receivers are thoroughly conducted for variable sun radiation and considering a broad range of working conditions. To achieve this goal, thermodynamic optimization of the chief factors was attained via a numerical model which calculated the energy efficiency of the Scheffler solar receiver at part-load working conditions by computing all energy losses negatively affecting the heat exchange phase in the cavity receiver. The results obtained in this study show that the solar collector efficiencies of Scheffler receivers appear more promising than that of usual parabolic trough collectors; moreover, Scheffler receivers persisted with less sensitivity to reductions in solar radiation intensity. For these reasons, solar power systems based on Scheffler-type systems can be used from tens to hundreds of kW to ensure the energetic supply of small urban settlements with acceptable efficiency, optimistic investments, simple construction and reduced overall sizes. [ABSTRACT FROM AUTHOR]

The text discusses the recent development and future perspectives of wind power generation, highlighting the rapid growth of wind energy globally. It covers topics such as wind potential assessment, wind power forecasting, wind power under climate change, socioeconomic and environmental factors of wind energy expansion, and wind turbine design development. The document emphasizes the need for improvements in research and the application of multidisciplinary approaches to address key challenges in wind energy utilization. The Special Issue 'Recent Development and Future Perspective of Wind Power Generation' provides valuable insights into these areas, closing some research gaps. [Extracted from the article]

Electrical power systems are the lifeblood of modern civilization, providing the essential energy infrastructure that powers our homes, industries, and technologies. As our world increasingly relies on electricity, and modern power systems incorporate renewable energy sources, the challenges have become more complex, necessitating advanced forecasting and optimization to ensure effective operation and sustainability. This review paper provides a comprehensive overview of electrical power systems and delves into the crucial roles that forecasting and optimization play in ensuring future sustainability. The paper examines various forecasting methodologies from traditional statistical approaches to advanced machine learning techniques, and it explores the challenges and importance of renewable energy forecasting. Additionally, the paper offers an in-depth look at various optimization problems in power systems including economic dispatch, unit commitment, optimal power flow, and network reconfiguration. Classical optimization methods and newer approaches such as meta-heuristic algorithms and artificial intelligence-based techniques are discussed. Furthermore, the review paper examines the integration of forecasting and optimization, demonstrating how accurate forecasts can enhance the effectiveness of optimization algorithms. This review serves as a reference for electrical engineers developing sophisticated forecasting and optimization techniques, leading to changing consumer behaviors, addressing environmental concerns, and ensuring a reliable, efficient, and sustainable energy future. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions from Thailand's biomass pellet production were comprehensively assessed, with a specific focus on wood and corn pellets. Employing the extended input and output tables, the anticipated economic and environmental effects of the rising demand for biomass pellets within the Asia–Pacific Economic Cooperation region, which is projected to see an increase exceeding 33% by the year 2050, were investigated. The estimations of CO2, CH4, and N2O emissions, which were conducted utilizing an open Leontief model based on the 2015 National Input–Output Tables, covered each stage of the production process. The results show that emissions from the production of corn pellets are expected to rise steadily, from 52.91 MtCO2e in 2022 to 75.77 MtCO2e by 2030, whereas emissions from wood pellet production are set to increase more substantially, from 210.30 to 301.18 MtCO2e within the same timeframe. Data derived from surveys and interviews with corn farmers and wood pellet manufacturers informed the lifecycle data for the biomass pellet supply chain from cradle to gate. The findings suggest that Thailand's power sector could benefit significantly from the biomass potential in the northern part of Thailand, which boasts an estimated energy content of corncob at 39 ktoe (0.0016 TJ). Market demand scenarios were explored in two forms: one where it was assumed that all biomass pellets are to be exported to Japan and South Korea, expecting a combined demand of approximately 560,262 tons by 2030, and another positing that 10% of production will be reserved for the domestic market, with a forecasted annual increase of 10% from 2020 to 2050. This paper highlights the need to prioritize low-emission renewable energy sources, expand technologies with lower lifecycle emissions, optimize the biomass supply chain to enhance efficiency, and introduce sustainable energy practices. The detailed GHG emissions analysis provides critical insights for policy formulation, underscoring the importance of sustainable transitions in the context of increasing biomass demand. [ABSTRACT FROM AUTHOR]

Decarbonization is driving power systems toward more decentralized, self-governing models. While these technologies improve efficiency, planning, operations, and reduce the carbon footprint, they also introduce new challenges. In modern grids, particularly with the integration of power electronic devices and high penetration of Renewable Energy Sources (RES) and Inverter-Based Resources (IBRs), traditional reliability concepts may no longer ensure adequate performance due to systemic restructuring. This shift necessitates new or significantly modified reliability indices to capture the characteristics of the evolving power system. Ensuring converter reliability is essential for effective planning, which requires precise, component-to-system-level modeling, as different converters impact system performance indicators. However, the existing literature in this field faces a significant limitation, as most studies focus on a singular perspective. Some examine reliability at the device-level, others at the component-level, while broader reviews in power systems often emphasize system-level analysis. In this paper, we aim to bridge these gaps by comprehensively reviewing the interconnections between these levels and analyzing the mutual influence of power converter and system reliability. A key point to highlight is that, with the rapid evolution of modern power grids, decision-makers must adopt a multi-level approach that incorporates insights from all levels to enable more accurate and realistic planning and operational strategies. Our ultimate goal is to provide an in-depth investigation of studies addressing the unique challenges posed by modern power grids. Finally, we will highlight the gaps in the literature and suggest directions for future research. [ABSTRACT FROM AUTHOR]

Under "dual-carbon" goals and rapid renewable energy growth, increasing start-stop frequency poses new challenges to safe operations of pumped-storage power plant equipment. Ensuring equipment safety and predictive maintenance under complex conditions urgently requires vibration warnings and trend forecasting for pumped-storage units. In this study, the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. We monitored the vibration-signal data of hydroelectric units over a long period of time, and the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are accurately obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. In this paper, a BP neural network prediction model, a support vector machine (SVM) prediction model, a convolutional neural network (CNN) prediction model, and a long short-term memory network (LSTM) prediction model are used to predict the trend of vibration signals of the pumped-storage unit under different operating conditions. The model prediction effect is analyzed by using the different error evaluation functions, and the prediction results are compared with the predicted results of the four different methods. By comparing the prediction effects of the four different methods, it is concluded that LSTM has higher prediction accuracy and can predict the vibration trends of hydropower units more accurately. [ABSTRACT FROM AUTHOR]

The main aim of the research was to assess Polish rural municipalities' investment activity connected with the development of a low-carbon economy, supported with EU funds in the 2007–2013 and 2014–2020 financial frameworks. The empirical study was based on data from Poland's Ministry of Development Funds and Regional Policy, Ministry of Finance, and Statistics Poland, analysed through basic descriptive statistics and a logistic model to identify key factors influencing investment activity. The study showed that the greatest number of agreements on funding were, in fact, signed by rural municipalities in the period under analysis. The predominant measures undertaken during this time were the promotion of renewable energy sources and the improvement in energy efficiency. In the earlier financial framework, low-carbon economy projects in rural areas were more often implemented by municipalities with developing demographic potential, including those characterised by a higher level of enterprise development. In the subsequent financial framework, human capital turned out to be of key importance for the investment activity of rural municipalities. Experience gained from 2007–2013 positively influenced fund absorption in 2014–2020, improving project value, number, and support. The study confirmed that rural municipalities play a vital role in advancing a low-carbon economy, as local actions are key to achieving sustainable development and reducing greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Over the past few decades, wind energy has expanded to become a widespread, clean, and sustainable energy source. However, integrating offshore wind energy with the onshore AC grids presents many stability and control challenges that hinder the reliability and resilience of AC grids, particularly during faults. To address this issue, current grid codes require offshore wind farms (OWFs) to remain connected during and after faults. This requirement is challenging because, depending on the fault location and power flow direction, DC link over- or under-voltage can occur, potentially leading to the shutdown of converter stations. Therefore, this necessitates the proper understanding of key technical concepts associated with the integration of OWFs. To help fill the gap, this article performs an in-depth investigation of existing alternating current fault ride through (ACFRT) techniques of modular multilevel converter-based high-voltage direct current (MMC-HVDC) for OWFs. These techniques include the use of AC/DC choppers, flywheel energy storage devices (FESDs), power reduction strategies for OWFs, and energy optimization of the MMC. This article covers both scenarios of onshore and offshore AC faults. Given the importance of wind turbines (WTs) in transforming wind energy into mechanical energy, this article also presents an overview of four WT topologies. In addition, this article explores the advanced converter topologies employed in HVDC systems to transform three-phase AC voltages to DC voltages and vice versa at each terminal of the DC link. Finally, this article explores the key stability and control concepts, such as small signal stability and large disturbance stability, followed by future research trends in the development of converter topologies for HVDC transmission such as hybrid HVDC systems, which combine current source converters (CSCs) and voltage source converters (VSCs) and diode rectifier-based HVDC (DR-HVDC) systems. [ABSTRACT FROM AUTHOR]

Countries have turned to developing renewable energy production, avoiding the risks posed by the disruptions in global energy trade, the high volatility in energy prices, and the remarkable environmental impairment. Numerous economic, environmental, institutional, and social factors have been put forward as driving factors toward renewable energy. The goal of this research article is to study the causal nexus among energy dependency, human capital, real GDP per capita, CO2 emissions, and renewable energy of the 27 EU members between 2000 and 2020 through Emirmahmutoglu and Kose causality test. The results of the panel-level causality tests demonstrate feedback interplay among energy dependency, human capital, real GDP per capita, CO2 emissions, and renewable energy use. However, the results of the country-level causality analysis unveil that the interplay among renewable energy utilization, energy dependency, real GDP per capita, CO2 emissions, and human capital remarkably varies among EU members. The results of this study suggest that renewable energy investments are significant instruments to make progress in energy security, human capital, real GDP per capita, and CO2 emissions. Furthermore, energy security, human capital, real GDP per capita, and CO2 emissions are significant drivers of renewable energy development. [ABSTRACT FROM AUTHOR]

High-voltage direct current (HVDC) transmission systems are widely used today due to their several advantages over high-voltage alternative current (HVAC) systems. They are primarily utilized in connecting different large grids or smaller independent networks, especially in submarine interconnections. Many studies have been conducted globally on the optimal planning, operation, and control of these power systems. Understanding the transient stability of these systems during significant disturbances is also of great importance. This paper specifically focuses on power systems with HVDC connections and high penetration of renewable energy sources. Analytical models were developed for power system components like HVDC converters, DC lines, and full converter wind generators. A simulation of a test-case power system was conducted, including various significant disturbances such as HVDC line trips, short circuits, power unit failures, and major changes in load. The voltage and frequency stability of the system under specific operational scenarios was also examined. The results obtained indicate technical limitations and operating guidelines to avoid undesirable conditions and system failure. In conclusion, adopting short-term stability tests the optimal operation point of the system, and the limitations in the penetration by RES units can be decided. [ABSTRACT FROM AUTHOR]

Emerging states' path to enhancing the welfare of their citizens has been strongly accompanied by environmental degradation; climate change effects often abrogate their economic results. This zero-sum game must change, and environmental concerns should be considered when the development of a country is discussed and assessed. In this sense, this study's objective is to analyze the impact of economic complexity and renewable energy consumption in the presence of economic growth and urbanization in selected emerging European countries from 1995 to 2021. We used a multiple-methodologic approach to highlight the supportive effects of economic complexity and renewable energy consumption in mitigating carbon emissions. Furthermore, the effects of economic growth and urbanization were emphasized by applying the cointegration regression (CCR), fully modified OLS, and dynamic OLS (FMOLS–DOLS) approaches. Additionally, we used Driscoll–Kraay estimation regression to test the robustness of our results. The results reveal the beneficial role of renewable energy consumption and economic complexity in the decarbonization process of selected countries. Furthermore, the study highlighted the detrimental influence of urbanization and economic growth, which were feasible considering the emerging status of the countries included in the panel. [ABSTRACT FROM AUTHOR]

Large CO2 emissions constitute a significant problem today due to their effect on climate change, and the need to design appropriate energy policies to mitigate their consequences and reduce emissions requires a detailed analysis of one of the main sources of such emissions: the electricity system. Thus, this paper presents a study on the effects of energy policies on decarbonization by comparing the detailed phase-out of coal-fired power plants across a range of cases with the implementation of a carbon tax to meet Nationally Determined Contributions (NDCs). The case study focuses on the Chilean electricity system, using a long-term generation and transmission expansion planning model (GTEP) that incorporates a wide range of generation technologies. The study examines the long-term effects of these policies, including costs, investments, and CO2 emissions, as well as their impact on consumer prices reflected in the marginal costs of the system. The transmission system modeling covers various regions of Chile and significant projections for renewable energy sources. It evaluates three economic scenarios based on generation technology costs, fuel prices, and electricity demand under four different closure schemes and fourteen different carbon tax levels. The results indicate that implementing a carbon tax can be more cost-effective for the system than the implementation of a phase-out schedule for coal plants, taking the form of reduced CO2 emission and overall system costs, with an optimal carbon tax value of 37 USD/tCO2. Additionally, the study reveals significant effects on consumer prices, showing that a carbon tax as an energy policy leads to lower prices compared to a phase-out scheme. [ABSTRACT FROM AUTHOR]

Despite being resource-richly endowed with various energy sources, and despite the connection of 89.8% of the households to the grid in South Africa, the Eastern Cape province, as compared to other provinces, has the lowest level of grid connection of about 64.5%. Some of the rural poor households in the Eastern Cape province supplement their free basic electricity with unclean energy alternatives. Using unclean energy alternatives is not only detrimental to the environment and health of the people, but it is a sign of energy poverty and among the contributing factors to depesantization, deagrarianization, and deindustrialization which prolongs the underdevelopment in rural areas. Innovation in energy technologies is a key ingredient in meaningful rural development. The utilization of small-scale biomass gasification technologies can be a solution to the South African energy crisis in rural areas, and it is in line with sustainable development goal number 7, which is about ensuring access to affordable, reliable, sustainable, and modern energy for all. Alternative renewable energy sources cannot be ignored when dealing with the energy crises in South Africa. Renewable energy sources in the country include biomass, solar, wind, and hydropower. Despite its low utilization in the Eastern Cape province, small-scale biomass gasification technology remains pivotal in reducing energy crisis by producing electricity. However, the affordability of biomass gasification technology also plays a role in whether people will accept small-scale biomass gasification technology. The purpose of this paper is to determine the possibilities of using small-scale biomass gasification technology. This paper gives a comprehensive review of small-scale biomass gasification technology potential in the Eastern Cape province and the link between acceptance of small-scale gasification technology and affordability by evaluating the availability of biomass sources in the province and achievements with regards to small-scale biomass gasification. This paper also covers the impact of biomass gasification technology integration in the energy grid, what needs to be taken into consideration before its installation, its benefits and the barriers to its development in Eastern Cape province. [ABSTRACT FROM AUTHOR]

As the scale of renewable energy sources (RESs) expands, it is essential to optimize the configuration of wind, solar, and storage resources across different areas. Nevertheless, the unavoidable uncertainties associated with both energy supply and demand present significant challenges for planners. This study aims to address the challenge of coordinated planning for multiarea wind-solar-energy storage systems considering multiple uncertainties. First, uncertainties related to future peak demand, thermal generation output boundaries, demand variability, and stochastic unit production are analyzed and modeled on the basis of robust optimization and stochastic programming techniques. Then, a hierarchical coordinated planning model that incorporates both system-wide (SW) and local area (LA) planning models is proposed. The SW planning model is designed to manage the optimal capacity configuration of RESs and energy storage systems (ESSs) within each LA, as well as the operational boundary of LAs. The LA planning models aim to further optimize the capacities of RESs and ESSs and minimize the economic cost within each LA on the basis of local resource characteristics. To achieve the optimal solution, the analytical target cascading (ATC) algorithm is integrated with the column-and-constraint generation (C&CG) algorithm. The simulation results validate the effectiveness and reasonableness of the proposed coordinated planning model, which not only outperforms independent planning approaches but also effectively manages the uncertainties. [ABSTRACT FROM AUTHOR]